Case-based learning: Predictive features in indexing

Interest in psychological experimentation from the Artificial Intelligence community often takes the form of rigorous post-hoc evaluation of completed computer models. Through an example of our own collaborative research, we advocate a different view of how psychology and AI may be mutually relevant, and propose an integrated approach to the study of learning in humans and machines. We begin with the problem of learning appropriate indices for storing and retrieving information from memory. From a planning task perspective, the most useful indices may be those that predict potential problems and access relevant plans in memory, improving the planner's ability to predict and avoid planning failures. This “predictive features” hypothesis is then supported as a psychological claim, with results showing that such features offer an advantage in terms of the selectivity of reminding because they more distinctively characterize planning situations where differing plans are appropriate.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46928/1/10994_2004_Article_BF00993173.pd

ISAC: A CBR system for decision support in air traffic control

The conflict resolution task performed by air-traffic controllers appears a suitable task for automation using CBR. This is because human competence seems to involve recognising situations and reusing solutions. In this paper we present our experiences in developing a CBR system to support this conflict resolution task. We discuss the problems of case representation: the macro problem of what should constitute a case and the micro problem of how to characterise a case. We evaluate some alternative case representations and identify a representation with one aircraft per case that is extendible to describe conflicts with multiple aircraft. Document type: Part of book or chapter of boo